Hi,
Be very careful with changing the orientation!
Did you follow the instructions on the FAQ?
It shows you how to do it by using both fslorient
(to change the header) and fslswapdim (to change
the data). You need to do both in order to keep
things consistent. If you don't you will have
changed which side is considered left/right which
is very bad for all the analyses. So make sure you
follow what the FAQ says and all will be well.
All the best,
Mark
On 24 May 2009, at 00:36, Jeremy Elman wrote:
> Ah, that was it! The files were in Neurological orientation. I
> changed the orientation in the header to Radiological and it worked
> just fine. As a side question, is it preferable to change the
> orientation in the header vs. changing the actual data?
>
> Thanks so much for the help!
> Jeremy
>
> Mark Jenkinson wrote:
>> Hi,
>>
>> The command line looks fine.
>> We did have a bug with this working for some neurologically-ordered
>> data. Can you run "fslorient" on the input data and tell me what the
>> result is?
>>
>> All the best,
>> Mark
>>
>>
>> On 23 May 2009, at 20:28, Jeremy Elman wrote:
>>
>>> Hi Mark,
>>>
>>> This is very helpful, as I had been wondering the same thing.
>>> However, when I ran this tool it seemed to find outlier volumes,
>>> but did not write an output file. Any ideas what I am doing wrong?
>>>
>>> Here is an example of my command line:
>>> fsl_motion_outliers /mtl/LDR/functional/LDR001-1/bold/004/f.nii 0 /
>>> mtl/LDR/functional/LDR001-1/bold/004/MotionOutliers.txt
>>>
>>> Thanks for your help,
>>> Jeremy
>>>
>>> Mark Jenkinson wrote:
>>>> Hi,
>>>>
>>>> There is a tool designed precisely for this.
>>>>
>>>> It is called fsl_motion_outliers and will check your motion
>>>> corrected data looking for points in time where there is an
>>>> unusual amount of residual intensity change (after motion
>>>> correction). Any outliers with respect to this are then
>>>> identified and a confound matrix created that you can
>>>> use in FEAT to effectively remove any changes associated
>>>> with these timepoints. Note that this is different from deleting
>>>> volumes as (i) it does not require adjusting the other model
>>>> EVs, and importantly, (ii) it correctly accounts for any changes
>>>> in signal and autocorrelation on either side of the "lost"
>>>> timepoint(s) as well as adjusting the degrees of freedom
>>>> correctly.
>>>>
>>>> To use it you just run fsl_motion_outliers on the original
>>>> (unfiltered and not motion corrected) data for each
>>>> subject/session individually. In each case it will create a
>>>> confound matrix which you add into the analysis for this
>>>> subject using the "Add additional confound EV(s)" button
>>>> on the "Stats" tab in FEAT. And that's it!
>>>>
>>>> Hope this sorts your problem out.
>>>> All the best,
>>>> Mark
>>>>
>>>>
>>>>
>>>>
>>>> On 21 May 2009, at 10:44, Klara Mareckova wrote:
>>>>
>>>>> Hello,
>>>>>
>>>>> do you happen to know if there is a relatively easy way in FSL
>>>>> how to
>>>>> idicate which slices and particular time series should be cut
>>>>> off from
>>>>> the analysis?
>>>>>
>>>>> I've analyzed the data for 50 subjects but found that they were
>>>>> moving
>>>>> a lot and therefore even if I would set quite lenient criteria and
>>>>> exclude everybody who moved more than 2 mm, I would end up with
>>>>> only
>>>>> 29 subjects. This is way too much and that is why I was thinking
>>>>> about
>>>>> cutting off the slices with the biggest movement (fslsplit
>>>>> &fslmerge).
>>>>> However, if I do this a problem with the time series comes out. Is
>>>>> there an easy way how to take care about this or do I have to go
>>>>> to
>>>>> each particular subject's design, exclude the particular time
>>>>> series
>>>>> and rerun the whole analysis?
>>>>>
>>>>> Do you also happen to have some guidelines about the exclusion
>>>>> criteria for motion correction? In some articles about adult
>>>>> participants I've seen exclusion criteria 1mm or 1degree but this
>>>>> seems to be too strict for my subjects.
>>>>>
>>>>> Many thanks for your help.
>>>>>
>>>>> Klara
>>>>>
>>>
>
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